Machine learning for condensed matter physics.

Area of Science:

• Condensed matter physics (CMP)
• Quantum mechanics
• Materials science

Background:

• CMP investigates microscopic interactions to understand macroscopic properties.
• Machine learning (ML) advancements offer new tools for CMP research.
• A growing field merges ML techniques with CMP challenges.

Purpose of the Study:

• To review successful applications of ML in condensed matter physics.
• To explore ML's role in potential energy surfaces, topological phases, and phase transitions.
• To discuss challenges and future perspectives of ML in CMP.

Main Methods:

• Review of ML schemes for potential energy surfaces.
• Characterization of topological phases using ML.
• ML for predicting phase transitions in simulations.
• Physics-inspired ML frameworks.
• ML algorithms enhancing simulation methods.

Main Results:

• ML techniques have been successfully applied across various CMP domains.
• ML aids in describing potential energy surfaces and characterizing topological phases.
• ML predicts phase transitions and enhances simulation efficiency.

Conclusions:

• ML offers powerful tools to advance condensed matter physics research.
• Addressing current challenges in ML for CMP is crucial for future progress.
• Interdisciplinary collaboration between ML and CMP is key for innovation.